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Proceedings Paper

Virtual clinical trial of lesion detection in digital mammography and digital breast tomosynthesis
Author(s): Predrag R. Bakic; Bruno Barufaldi; David Higginbotham; Susan P. Weinstein; Ali N. Avanaki; Kathryn S. Espig; Albert Xthona; Tom R. L. Kimpe; Andrew D. A. Maidment
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Paper Abstract

We have designed and conducted 35 virtual clinical trials (VCTs) of breast lesion detection in digital mammography (DM) and digital breast tomosynthesis (DBT) using a novel open-source simulation pipeline, OpenVCT. The goal of the VCTs is to test in-silico reports that DBT provides substantial improvements in the detectability of masses, while the detectability of microcalcifications remains comparable to DM. For this test, we generated 12 software breast phantoms (volume 700ml, compressed thickness 6.33cm), varying the number of simulated tissue compartments and their shape. Into each phantom, we inserted multiple lesions located 2cm apart in the plane parallel to detector at the level of the nipple. Simulated ellipsoidal masses (oblate spheroids 7mm in diameter and of various thicknesses) and single calcifications of various size and composition were inserted; a total of 17,640 lesions were simulated for this project. DM and DBT projections of phantoms with and without lesions were synthesized assuming a clinical acquisition geometry. Exposure parameters (mAs and kVp) were selected to match AEC settings. Processed DM images and reconstructed DBT slices were obtained using a commercially available software library. Lesion detection was simulated by channelized Hotelling observers, with 15 LG channels and a spread of 22, using independent sets of 480 image samples (150×150 pixel ROIs) for training and 480 samples for testing. Our VCTs showed an average AUC improvement for DBT vs DM of 0.027 for microcalcifications and 0.103 for masses, in close agreement (within 1%) of clinical data reported in the literature.

Paper Details

Date Published: 9 March 2018
PDF: 13 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057306 (9 March 2018); doi: 10.1117/12.2294934
Show Author Affiliations
Predrag R. Bakic, The Univ. of Pennsylvania (United States)
Bruno Barufaldi, The Univ. of Pennsylvania (United States)
David Higginbotham, The Univ. of Pennsylvania (United States)
Susan P. Weinstein, The Univ. of Pennsylvania (United States)
Ali N. Avanaki, Barco, Inc. (United States)
Kathryn S. Espig, Barco, Inc. (United States)
Albert Xthona, Barco, Inc. (United States)
Tom R. L. Kimpe, Barco, Inc. (Belgium)
Andrew D. A. Maidment, The Univ. of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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